In order to obtain real-time controlling dynamics in air traffic system, a framework is proposed to introduce and process air traffic control (ATC) speech via radiotelephony communication. An automatic speech recognition (ASR) and controlling instruction understanding (CIU)-based pipeline is designed to convert the ATC speech into ATC related elements, i.e., controlling intent and parameters. A correction procedure is also proposed to improve the reliability of the information obtained by the proposed framework. In the ASR model, acoustic model (AM), pronunciation model (PM), and phoneme- and word-based language model (LM) are proposed to unify multilingual ASR into one model. In this work, based on their tasks, the AM and PM are defined as speech recognition and machine translation problems respectively. Two-dimensional convolution and average-pooling layers are designed to solve special challenges of ASR in ATC. An encoder–decoder architecture-based neural network is proposed to translate phoneme labels into word labels, which achieves the purpose of ASR. In the CIU model, a recurrent neural network-based joint model is proposed to detect the controlling intent and label the controlling parameters, in which the two tasks are solved in one network to enhance the performance with each other based on ATC communication rules. The ATC speech is now converted into ATC related elements by the proposed ASR and CIU model. To further improve the accuracy of the sensing framework, a correction procedure is proposed to revise minor mistakes in ASR decoding results based on the flight information, such as flight plan, ADS-B. The proposed models are trained using real operating data and applied to a civil aviation airport in China to evaluate their performance. Experimental results show that the proposed framework can obtain real-time controlling dynamics with high performance, only 4% word-error rate. Meanwhile, the decoding efficiency can also meet the requirement of real-time applications, i.e., an average 0.147 real time factor. With the proposed framework and obtained traffic dynamics, current ATC applications can be accomplished with higher accuracy. In addition, the proposed ASR pipeline has high reusability, which allows us to apply it to other controlling scenes and languages with minor changes.

Automatic Speech Recognition (ASR) is greatly developed in recent years, which expedites many applications on other fields. For the ASR research, speech corpus is always an essential foundation, especially for the vertical industry, such as Air Traffic Control (ATC). There are some speech corpora for common applications, public or paid. However, for the ATC, it is difficult to collect raw speeches from real systems due to safety issues. More importantly, for a supervised learning task like ASR, annotating the transcription is a more laborious work, which hugely restricts the prospect of ASR application. In this paper, a multilingual speech corpus (ATCSpeech) from real ATC systems, including accented Mandarin Chinese and English, is built and released to encourage the non-commercial ASR research in ATC domain. The corpus is detailly introduced from the perspective of data amount, speaker gender and role, speech quality and other attributions. In addition, the performance of our baseline ASR models is also reported. A community edition for our speech database can be applied and used under a special contrast. To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.